Weekly Review: COVID-19 Data and Surveillance – May 17, 2021

Weekly Review: COVID-19 Data and Surveillance – May 17, 2021

Vaccine Effectiveness

This observational study evaluates the effectiveness of BNT162b2 (Pfizer vaccine) among health care workers in an area with a high rate of the B.1.1.7 variant in Northern Italy during the time period of January 25, 2021 through April 13, 2021.   Approximately 6904 health care workers (78% of workforce) had gained full protection at the time.  In order to evaluate the effectiveness of the vaccine, infection and  symptoms were monitored among health care workers.  The findings showed that at the end of the observation period, vaccinated health care workers were at a 2.6 times lower risk of infection compared to unvaccinated health care workers.  The study also found that the incidence rate of the B.1.1.7  variant rose from 70% (February 18, 2021) to 97% (March 28, 2021).  Lastly, a a reduction in infection rate was seen with the unvaccinated healthcare workers suggesting partial herd immunity in that specific population.

Environmental and Socioeconomic Factors

The authors of this article aim to provide scientific evidence based on statistical modeling regarding the spreading of COVID-19 in the context of constantly changing factors that include humidity, temperatures, population density, GDP, life expectancy, total tests, air quality index, and particulate matter, specifically PM2.5 and PM10.  Data were collected from 126 cities and provinces which were in 42 of the most affected countries.  Negative Binomial and Poisson models were used for data analysis.  The results indicated that there could be a relationship between environmental factors and incidence of COVID-19.  For example, majority of the cities where initial outbreaks occurred such as Madrid, New York City, etc., had low temperatures as well as low levels of humidity whereas places that had high levels of humidity had lower rates of cases (Banten, Central Luzon, etc.).  Contrarily, Riyadh had high levels of humidity and high levels of cases most likely due to population density and mobility.  According to Pearson correlation coefficients, a strong positive correlation between average high and low temperatures was observed. Furthermore, GDP and health expenditure were positively correlated along with particulate matter.   Additional results include: population density (coefficient estimate: 0.135; 95% CI: (0.019, 0.255), p-value = 0.021), GDP (coefficient estimate:−1.631; 95% CI: (−2.931, −0.375), p-value = 0.021), PM10 (coefficient estimate:0.017; 95% CI: (0.002, 0.033), p-value =.011), PM2.5 (coefficient estimate: −0.022; 95% CI: (−0.037, −0.006), p-value = 0.001), total number of tests (coefficient estimate: 0.809; 95% CI: (0.610, 1.008), p-value = 0.000).  These were significantly associated with COVID-19.  The results further suggest that for every unit increase in GDP, there was a significant decrease in case count of 80.4%. Specific to particulate matter, the authors suggest that the percent change in the incident rate of cases is a 2.2% decrease for every unit increase in PM2.5.  Also, each unit increase in PM10 increased the COVID-19 infected case count by a 1.7% increase.

 

 

 

 

 

 

 

 

 

|2021-05-17T09:43:50-04:00May 17th, 2021|COVID-19 Literature|0 Comments

About the Author: Payal Patel-Dovlatabadi

Payal Patel-Dovlatabadi
Payal Patel-Dovlatabadi, DrPH, MPH, MBA is an Associate Professor of Public Health and Director of the undergraduate and graduate programs in public health at the University of Evansville. She serves as the public health expert for local media and has appeared on over 100 televised interviews regarding various public health issues with over 50 of the interviews related to COVID-19. Her research interests include healthcare systems and policies in the comparative perspective related to social epidemiology.

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